From: Nico Galoppo (nico_at_[hidden])
Date: 2007-02-13 17:18:47
I believe CXSparse doesn't do reordering, so you'll have problems with fill-in.
I think UMFPACK is a better choice, or use something like METIS to do the
reordering for you.
Paul C. Leopardi wrote:
> Hi all,
> Perhaps cs_qr.c from
> could be adapted? It is LGPL:
> See "Direct Methods for Sparse Linear Systems: the CXSparse package"
> Tim Davis, http://www.cise.ufl.edu/research/sparse/CXSparse/
> Direct Methods for Sparse Linear Systems, T. A. Davis, SIAM, Philadelphia,
> Sept. 2006.
> I have not tried cs_qr.c myself.
> See also
> MR1438098 (98b:65049) Matstoms, Pontus
> "Sparse linear least squares problems in optimization."
> Computational issues in high performance software for nonlinear optimization
> (Capri, 1995). Comput. Optim. Appl. 7 (1997), no. 1, 89--110.
> Pontus Matstoms,
> "Sparse QR factorization in MATLAB", 1994,
> Best, Paul
> On Wed, 14 Feb 2007, Karl Meerbergen wrote:
>> Gunter Winkler wrote:
>>> Am Dienstag, 13. Februar 2007 00:28 schrieb
>>>> I have a very sparse matrix (<= 6 nnz entries per row with
>>>> potentially thousands of columns) which I need to pre-multiply by its
>>>> transpose in order to solve the normal equations associated with a
>>>> least-squares computation: (A'A)x = A'b. I may have missed them, but
>>> Don't do this - trust me ;-)
>>> in order to solve the least squares problem you do a QR-decomposition of
>>> A and get the cholesky decomposition of A'A for free:
>>> A = QR -> A'A = (QR)'QR = R'Q'QR= R'R
>> True. But you need a sparse QR factorization. It exists, but I do not
>> recall precisely where you can find code. A sparse QR can be done with a
>> sparse Gram-Schmidt routine. Needless to say that fill-in is going to
>> grow. Developing an efficient code using BLAS 3 etc is quite a job.
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